{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 择时信号研究\n",
    "一、 数据读取与处理\n",
    "\n",
    "二、 画图观察与构思算法\n",
    "\n",
    "三、 进场&时间出场&&止损止盈"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、数据读取与处理\n",
    "1. 数据读取并转换成jaqs需要的数据格式\n",
    "3. 数据合并成不同周期"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 数据读取并转换成jaqs需要的数据格式\n",
    "    1. reset_index\n",
    "    2. datetime to int\n",
    "    3. add symbol column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "HFData = pd.read_excel('HFData1M.xlsx').set_index('datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reset_index\n",
    "# dataResample\n",
    "def dt2int(t):\n",
    "    return t.year*10**10+t.month*10**8+t.day*10**6+t.hour*10**4+t.minute*10**2+t.second\n",
    "\n",
    "def modifyDf(data, symbol):\n",
    "    assert isinstance(data, pd.DataFrame)\n",
    "    data = data.reset_index()\n",
    "    data['trade_date'] = data['datetime'].apply(dt2int)\n",
    "    data['symbol'] = symbol\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataIf = modifyDf(HFData, 'IF88_CTP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>exchange</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>openInterest</th>\n",
       "      <th>symbol</th>\n",
       "      <th>time</th>\n",
       "      <th>trading_date</th>\n",
       "      <th>volume</th>\n",
       "      <th>vtSymbol</th>\n",
       "      <th>trade_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43435</th>\n",
       "      <td>2019-02-28 14:55:00</td>\n",
       "      <td>3678.8</td>\n",
       "      <td>20190228</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3680.4</td>\n",
       "      <td>3678.6</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>69005</td>\n",
       "      <td>IF88_CTP</td>\n",
       "      <td>14:55:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>292</td>\n",
       "      <td>IF88:CTP</td>\n",
       "      <td>20190228145500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43436</th>\n",
       "      <td>2019-02-28 14:56:00</td>\n",
       "      <td>3680.0</td>\n",
       "      <td>20190228</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3681.4</td>\n",
       "      <td>3678.6</td>\n",
       "      <td>3678.8</td>\n",
       "      <td>69149</td>\n",
       "      <td>IF88_CTP</td>\n",
       "      <td>14:56:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>344</td>\n",
       "      <td>IF88:CTP</td>\n",
       "      <td>20190228145600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43437</th>\n",
       "      <td>2019-02-28 14:57:00</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>20190228</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>3679.8</td>\n",
       "      <td>69314</td>\n",
       "      <td>IF88_CTP</td>\n",
       "      <td>14:57:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>437</td>\n",
       "      <td>IF88:CTP</td>\n",
       "      <td>20190228145700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43438</th>\n",
       "      <td>2019-02-28 14:58:00</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>20190228</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3684.4</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>69540</td>\n",
       "      <td>IF88_CTP</td>\n",
       "      <td>14:58:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>518</td>\n",
       "      <td>IF88:CTP</td>\n",
       "      <td>20190228145800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43439</th>\n",
       "      <td>2019-02-28 14:59:00</td>\n",
       "      <td>3677.4</td>\n",
       "      <td>20190228</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3681.2</td>\n",
       "      <td>3677.2</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>69794</td>\n",
       "      <td>IF88_CTP</td>\n",
       "      <td>14:59:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>605</td>\n",
       "      <td>IF88:CTP</td>\n",
       "      <td>20190228145900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime   close      date exchange    high     low    open  \\\n",
       "43435 2019-02-28 14:55:00  3678.8  20190228      CTP  3680.4  3678.6  3679.6   \n",
       "43436 2019-02-28 14:56:00  3680.0  20190228      CTP  3681.4  3678.6  3678.8   \n",
       "43437 2019-02-28 14:57:00  3682.0  20190228      CTP  3683.0  3679.6  3679.8   \n",
       "43438 2019-02-28 14:58:00  3679.6  20190228      CTP  3684.4  3679.6  3682.0   \n",
       "43439 2019-02-28 14:59:00  3677.4  20190228      CTP  3681.2  3677.2  3679.6   \n",
       "\n",
       "       openInterest    symbol      time trading_date  volume  vtSymbol  \\\n",
       "43435         69005  IF88_CTP  14:55:00   2019-02-28     292  IF88:CTP   \n",
       "43436         69149  IF88_CTP  14:56:00   2019-02-28     344  IF88:CTP   \n",
       "43437         69314  IF88_CTP  14:57:00   2019-02-28     437  IF88:CTP   \n",
       "43438         69540  IF88_CTP  14:58:00   2019-02-28     518  IF88:CTP   \n",
       "43439         69794  IF88_CTP  14:59:00   2019-02-28     605  IF88:CTP   \n",
       "\n",
       "           trade_date  \n",
       "43435  20190228145500  \n",
       "43436  20190228145600  \n",
       "43437  20190228145700  \n",
       "43438  20190228145800  \n",
       "43439  20190228145900  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataIf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数据合并成不同周期\n",
    "    1. resample\n",
    "    2. agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def resampleDf(data, freq='H'):\n",
    "    return data.resample(freq).agg({'open':'first', 'high': 'max', 'low': 'min', 'close': 'last', 'volume':'sum'})\n",
    "\n",
    "dataResample = resampleDf(HFData, '15Min')\n",
    "dataIf15 = modifyDf(dataResample, 'IF88_CTP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26129</th>\n",
       "      <td>2019-02-28 13:45:00</td>\n",
       "      <td>3676.4</td>\n",
       "      <td>3681.6</td>\n",
       "      <td>3668.0</td>\n",
       "      <td>3668.6</td>\n",
       "      <td>4641.0</td>\n",
       "      <td>20190228134500</td>\n",
       "      <td>IF88_CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26130</th>\n",
       "      <td>2019-02-28 14:00:00</td>\n",
       "      <td>3668.6</td>\n",
       "      <td>3683.8</td>\n",
       "      <td>3667.2</td>\n",
       "      <td>3679.2</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>20190228140000</td>\n",
       "      <td>IF88_CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26131</th>\n",
       "      <td>2019-02-28 14:15:00</td>\n",
       "      <td>3679.2</td>\n",
       "      <td>3680.8</td>\n",
       "      <td>3664.2</td>\n",
       "      <td>3678.6</td>\n",
       "      <td>4146.0</td>\n",
       "      <td>20190228141500</td>\n",
       "      <td>IF88_CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26132</th>\n",
       "      <td>2019-02-28 14:30:00</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>3687.4</td>\n",
       "      <td>3668.4</td>\n",
       "      <td>3671.2</td>\n",
       "      <td>4397.0</td>\n",
       "      <td>20190228143000</td>\n",
       "      <td>IF88_CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26133</th>\n",
       "      <td>2019-02-28 14:45:00</td>\n",
       "      <td>3671.2</td>\n",
       "      <td>3684.4</td>\n",
       "      <td>3667.2</td>\n",
       "      <td>3677.4</td>\n",
       "      <td>4624.0</td>\n",
       "      <td>20190228144500</td>\n",
       "      <td>IF88_CTP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime    open    high     low   close  volume  \\\n",
       "26129 2019-02-28 13:45:00  3676.4  3681.6  3668.0  3668.6  4641.0   \n",
       "26130 2019-02-28 14:00:00  3668.6  3683.8  3667.2  3679.2  3424.0   \n",
       "26131 2019-02-28 14:15:00  3679.2  3680.8  3664.2  3678.6  4146.0   \n",
       "26132 2019-02-28 14:30:00  3679.0  3687.4  3668.4  3671.2  4397.0   \n",
       "26133 2019-02-28 14:45:00  3671.2  3684.4  3667.2  3677.4  4624.0   \n",
       "\n",
       "           trade_date    symbol  \n",
       "26129  20190228134500  IF88_CTP  \n",
       "26130  20190228140000  IF88_CTP  \n",
       "26131  20190228141500  IF88_CTP  \n",
       "26132  20190228143000  IF88_CTP  \n",
       "26133  20190228144500  IF88_CTP  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataIf15.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 整数转Datetime\n",
    "from datetime import datetime\n",
    "dataIf15['datetimeType'] = list(map(lambda x : datetime.strptime(str(x), \"%Y%m%d%H%M%S\"), dataIf15.trade_date))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、 算法与画图观察\n",
    "1. 安装jaqs_fxdayu\n",
    "2. 将数据导入HFDataView\n",
    "3. 算法使用与画图观察\n",
    "4. talib\n",
    "5. 画图去除停牌时间\n",
    "6. 观察并设计算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 安装jaqs_fxdayu: \n",
    "pip install git+https://github.com/xingetouzi/jaqs-fxdayu.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\importlib\\__init__.py:126: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return _bootstrap._gcd_import(name[level:], package, level)\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "from jaqs_fxdayu.data.hf_dataview import HFDataView"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 将数据导入HFDataView\n",
    "    1. instantiation\n",
    "    2. create_inti_dv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialize dataview success.\n"
     ]
    }
   ],
   "source": [
    "# instantiation\n",
    "dv = HFDataView()\n",
    "# create_inti_dv\n",
    "dv.create_init_dv(dataIf.dropna().set_index(['trade_date', 'symbol']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialize dataview success.\n"
     ]
    }
   ],
   "source": [
    "dv15 = HFDataView()\n",
    "dv15.create_init_dv(dataIf15.dropna().set_index(['trade_date', 'symbol']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['+', '-', '*', '/', 'Sign(x)', 'Abs(x)', 'Log(x)', '-x', '^',\n",
       "       'Pow(x,y)', 'SignedPower(x,e)', '%', '==', '!=', '>', '<', '>=',\n",
       "       '<=', '&&', '||', '!', 'IsNan(x)', 'Sin(x)', 'Cos(x)', 'Tan(x)',\n",
       "       'Sqrt(x)', 'Ceil(x)', 'Floor(x)', 'Round(x)', 'Max(x,y)',\n",
       "       'Min(x,y)', 'If(cond,x,y)', 'Delay(x,n)', 'Ts_Sum(x,n)',\n",
       "       'Ts_Product(x,n)', 'Delta(x,n)', 'Return(x,n,log)', 'Ts_Mean(x，n)',\n",
       "       'StdDev(x,n)', 'Covariance(x,y,n)', 'Correlation(x,y,n)',\n",
       "       'Ts_Min(x，n)', 'Ts_Max(x，n)', 'Ts_Skewness(x，n)',\n",
       "       'Ts_Kurtosis(x，n)', 'Ts_Rank(x, n)', 'Ts_Percentile(x, n)',\n",
       "       'Ts_Quantile(x, n)', 'Ewma(x, halflife)', 'Rank(x)',\n",
       "       'GroupRank(x,g)', 'Percentile(x)', 'GroupPercentile(x, g, n)',\n",
       "       'ConditionRank(x, cond)', 'Quantile(x, n)',\n",
       "       'GroupQuantile(x, g, n)', 'Standardize(x)', 'Cutoff(x, z_score)',\n",
       "       'CumToSingle(x)', 'TTM(x)', 'Decay_exp(x,f,n)',\n",
       "       'Decay_linear(x,n)', 'Tail(x, lower, upper, newval)', 'Step(n)',\n",
       "       'CountNans(x,n)', 'Ts_Argmax(x,n)', 'Ts_Argmin(x,n)',\n",
       "       'Ta(ta_method,ta_column,open,high,low,close,volume,*args)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看函数\n",
    "dv.func_doc.funcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "      <th>说明</th>\n",
       "      <th>公式</th>\n",
       "      <th>示例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>选择函数</td>\n",
       "      <td>cond为True取x的值，反之取y的值</td>\n",
       "      <td>If(cond,x,y)</td>\n",
       "      <td>If(close &gt; open, close, open) 表示取open和close的较大值</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      分类                    说明            公式  \\\n",
       "31  选择函数  cond为True取x的值，反之取y的值  If(cond,x,y)   \n",
       "\n",
       "                                                 示例  \n",
       "31  If(close > open, close, open) 表示取open和close的较大值  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看函数定义\n",
    "dv.func_doc.doc[dv.func_doc.doc['公式']=='If(cond,x,y)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"12\" halign=\"left\">IF88_CTP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>field</th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>datetime</th>\n",
       "      <th>exchange</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>openInterest</th>\n",
       "      <th>time</th>\n",
       "      <th>trading_date</th>\n",
       "      <th>volume</th>\n",
       "      <th>vtSymbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20190228145500</th>\n",
       "      <td>3678.8</td>\n",
       "      <td>20190228</td>\n",
       "      <td>2019-02-28 14:55:00</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3680.4</td>\n",
       "      <td>3678.6</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>69005</td>\n",
       "      <td>14:55:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>292</td>\n",
       "      <td>IF88:CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145600</th>\n",
       "      <td>3680.0</td>\n",
       "      <td>20190228</td>\n",
       "      <td>2019-02-28 14:56:00</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3681.4</td>\n",
       "      <td>3678.6</td>\n",
       "      <td>3678.8</td>\n",
       "      <td>69149</td>\n",
       "      <td>14:56:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>344</td>\n",
       "      <td>IF88:CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145700</th>\n",
       "      <td>3682.0</td>\n",
       "      <td>20190228</td>\n",
       "      <td>2019-02-28 14:57:00</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>3679.8</td>\n",
       "      <td>69314</td>\n",
       "      <td>14:57:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>437</td>\n",
       "      <td>IF88:CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145800</th>\n",
       "      <td>3679.6</td>\n",
       "      <td>20190228</td>\n",
       "      <td>2019-02-28 14:58:00</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3684.4</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>69540</td>\n",
       "      <td>14:58:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>518</td>\n",
       "      <td>IF88:CTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145900</th>\n",
       "      <td>3677.4</td>\n",
       "      <td>20190228</td>\n",
       "      <td>2019-02-28 14:59:00</td>\n",
       "      <td>CTP</td>\n",
       "      <td>3681.2</td>\n",
       "      <td>3677.2</td>\n",
       "      <td>3679.6</td>\n",
       "      <td>69794</td>\n",
       "      <td>14:59:00</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>605</td>\n",
       "      <td>IF88:CTP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol         IF88_CTP                                                 \\\n",
       "field             close      date            datetime exchange    high   \n",
       "trade_date                                                               \n",
       "20190228145500   3678.8  20190228 2019-02-28 14:55:00      CTP  3680.4   \n",
       "20190228145600   3680.0  20190228 2019-02-28 14:56:00      CTP  3681.4   \n",
       "20190228145700   3682.0  20190228 2019-02-28 14:57:00      CTP  3683.0   \n",
       "20190228145800   3679.6  20190228 2019-02-28 14:58:00      CTP  3684.4   \n",
       "20190228145900   3677.4  20190228 2019-02-28 14:59:00      CTP  3681.2   \n",
       "\n",
       "symbol                                                                     \\\n",
       "field              low    open openInterest      time trading_date volume   \n",
       "trade_date                                                                  \n",
       "20190228145500  3678.6  3679.6        69005  14:55:00   2019-02-28    292   \n",
       "20190228145600  3678.6  3678.8        69149  14:56:00   2019-02-28    344   \n",
       "20190228145700  3679.6  3679.8        69314  14:57:00   2019-02-28    437   \n",
       "20190228145800  3679.6  3682.0        69540  14:58:00   2019-02-28    518   \n",
       "20190228145900  3677.2  3679.6        69794  14:59:00   2019-02-28    605   \n",
       "\n",
       "symbol                    \n",
       "field           vtSymbol  \n",
       "trade_date                \n",
       "20190228145500  IF88:CTP  \n",
       "20190228145600  IF88:CTP  \n",
       "20190228145700  IF88:CTP  \n",
       "20190228145800  IF88:CTP  \n",
       "20190228145900  IF88:CTP  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 算法使用与画图观察\n",
    "    1. add_formula\n",
    "    2. get_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add_formula\n",
    "gap = dv.add_formula('Gap','open-Delay(close, 1)', add_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dv.add_formula?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>symbol</th>\n",
       "      <th>IF88_CTP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20190228145500</th>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145600</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145700</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145800</th>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20190228145900</th>\n",
       "      <td>-4.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol          IF88_CTP\n",
       "trade_date              \n",
       "20190228145500      -1.6\n",
       "20190228145600       0.4\n",
       "20190228145700       3.2\n",
       "20190228145800      -0.2\n",
       "20190228145900      -4.6"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# algorithm\n",
    "gap.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get_ts\n",
    "gapDt = dv.get_ts('Gap', date_type='datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dv.get_ts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>symbol</th>\n",
       "      <th>IF88_CTP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-28 14:55:00</th>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28 14:56:00</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28 14:57:00</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28 14:58:00</th>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28 14:59:00</th>\n",
       "      <td>-4.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol               IF88_CTP\n",
       "trade_date                   \n",
       "2019-02-28 14:55:00      -1.6\n",
       "2019-02-28 14:56:00       0.4\n",
       "2019-02-28 14:57:00       3.2\n",
       "2019-02-28 14:58:00      -0.2\n",
       "2019-02-28 14:59:00      -4.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapDt.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sws9e3oRHvtiv+bXSTaDTjRbcPmtehyZXOHsiiiIe+GQPDsS0nC6KbFg3mslr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\nYZBHREREREQUIQzyiIiIiIiIIuT/B/6MrR2AY17cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218f30cbbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "plt.plot(gapDt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. talib\n",
    "\n",
    "dv.add_formula('name', \"Ta('Function',0,open, high, low, close, volume)\",add_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "macdDv = dv15.add_formula('macd', \"Ta('MACD',0,open, high, low, close, volume, 12, 26, 9)\",add_data=True)\n",
    "macdSignalDv = dv15.add_formula('macdSignal', \"Ta('MACD',1,open, high, low, close, volume, 12, 26, 9)\",add_data=True)\n",
    "macdHistDv = dv15.add_formula('macdHist', \"Ta('MACD',2,open, high, low, close, volume, 12, 26, 9)\",add_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 生成DataFrame\n",
    "DataDf = pd.DataFrame(\n",
    "                        {\n",
    "                        'close':dv15.get_ts('close', date_type = 'datetime')['IF88_CTP'],\n",
    "                        'macd' : dv15.get_ts('macd', date_type='datetime')['IF88_CTP'],\n",
    "                        'macdSignal':dv15.get_ts('macdSignal', date_type='datetime')['IF88_CTP'],\n",
    "                        'macdHist':dv15.get_ts('macdHist', date_type='datetime')['IF88_CTP']\n",
    "                        }\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def chartRange(df, n=100):\n",
    "    fig, (ax, ax1) = plt.subplots(2, 1, sharex=True, figsize=(15,10))\n",
    "    ax.plot(df.index[-n:], DataDf.close.iloc[-n:])\n",
    "    ax1.plot(df.index[-n:], DataDf.macd.iloc[-n:])\n",
    "    ax1.plot(df.index[-n:], DataDf.macdSignal.iloc[-n:])\n",
    "    ax1.bar(df.index[-n:], DataDf.macdHist.iloc[-n:], color='green', width=0.8)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yv338dI2e7fXGtRMAABmJ0UzLbIDBHQVFiqu3WlZKrF56v2EQV1SpNtPu4tq3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VmR+W6bk+sv3R38k/QHJ3zlEzyY2jCvRILnvZQjqaMlywvHB+8lNxMSACFrXsZbFtIBc6dvVk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\nEREREZEaoORORERERESkBvx/KVBAlC3FvJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218f325fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chartRange(DataDf, 5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 去除停盘时间\n",
    "    1. reset_index\n",
    "    2. strftime时间格式转成字符串\n",
    "    3. xticks设置x轴索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataIf1 = DataDf.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# strftime时间格式转成字符串\n",
    "dataIf1['tradeDateStr'] = list(map(lambda x: x.strftime(format='%Y-%m-%d %H:%M:%S'), list(dataIf1.trade_date)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>close</th>\n",
       "      <th>macd</th>\n",
       "      <th>macdHist</th>\n",
       "      <th>macdSignal</th>\n",
       "      <th>tradeDateStr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2891</th>\n",
       "      <td>2019-02-28 13:45:00</td>\n",
       "      <td>3668.6</td>\n",
       "      <td>-5.885688</td>\n",
       "      <td>-2.157227</td>\n",
       "      <td>-3.728461</td>\n",
       "      <td>2019-02-28 13:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2019-02-28 14:00:00</td>\n",
       "      <td>3679.2</td>\n",
       "      <td>-6.112784</td>\n",
       "      <td>-1.907458</td>\n",
       "      <td>-4.205326</td>\n",
       "      <td>2019-02-28 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2893</th>\n",
       "      <td>2019-02-28 14:15:00</td>\n",
       "      <td>3678.6</td>\n",
       "      <td>-6.268910</td>\n",
       "      <td>-1.650868</td>\n",
       "      <td>-4.618043</td>\n",
       "      <td>2019-02-28 14:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2894</th>\n",
       "      <td>2019-02-28 14:30:00</td>\n",
       "      <td>3671.2</td>\n",
       "      <td>-6.910104</td>\n",
       "      <td>-1.833649</td>\n",
       "      <td>-5.076455</td>\n",
       "      <td>2019-02-28 14:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>2019-02-28 14:45:00</td>\n",
       "      <td>3677.4</td>\n",
       "      <td>-6.839130</td>\n",
       "      <td>-1.410140</td>\n",
       "      <td>-5.428990</td>\n",
       "      <td>2019-02-28 14:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              trade_date   close      macd  macdHist  macdSignal  \\\n",
       "2891 2019-02-28 13:45:00  3668.6 -5.885688 -2.157227   -3.728461   \n",
       "2892 2019-02-28 14:00:00  3679.2 -6.112784 -1.907458   -4.205326   \n",
       "2893 2019-02-28 14:15:00  3678.6 -6.268910 -1.650868   -4.618043   \n",
       "2894 2019-02-28 14:30:00  3671.2 -6.910104 -1.833649   -5.076455   \n",
       "2895 2019-02-28 14:45:00  3677.4 -6.839130 -1.410140   -5.428990   \n",
       "\n",
       "             tradeDateStr  \n",
       "2891  2019-02-28 13:45:00  \n",
       "2892  2019-02-28 14:00:00  \n",
       "2893  2019-02-28 14:15:00  \n",
       "2894  2019-02-28 14:30:00  \n",
       "2895  2019-02-28 14:45:00  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataIf1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def chartRange(data, n=100):\n",
    "    fig, (ax, ax1) = plt.subplots(2, 1, sharex=True, figsize=(15,10))\n",
    "    ax.plot(data.index[-n:], data.close.iloc[-n:])\n",
    "    ax1.plot(data.index[-n:], data.macd.iloc[-n:])\n",
    "    ax1.plot(data.index[-n:], data.macdSignal.iloc[-n:])\n",
    "    ax1.bar(data.index[-n:], data.macdHist.iloc[-n:], color='green', width=0.8)\n",
    "    plt.xticks([data.index[-n:][0], data.index[-n:][int(n/2)], data.index[-n:][-1]],\n",
    "               [data.tradeDateStr.iloc[0],data.tradeDateStr.iloc[int(n/2)], data.tradeDateStr.iloc[-1]])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ACdzgqRgKcD3wPO6Ko/NQxVARERERkW7P4XQR5K8gsDs5ZCBorV0DTGimvR647ADn3Avc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\nKX15Zdl2KhxOiqvriQsL7ND+ikjnef7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daZqc/qMlnD+5gJ/dOHe0hyOnkFA4woY6D9f8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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x218fc364128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chartRange(dataIf1, n=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 观察并设计算法\n",
    "    1. 0轴区分\n",
    "    2. 金叉死叉\n",
    "    3. 二次金叉与死叉\n",
    "    4. macd与macdSignal同涨同跌\n",
    "    5. macdHist的下降与上升\n",
    "    6. macdHist第五元素"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "策略一\n",
    "long: (macd[-1]>0 and goldenCross) \n",
    "\n",
    "short: (macd[-1]<0 and deathCross) "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "策略二\n",
    "long: macd[-1]>0 and macd[-2]<0 and macd[-1]>macd[-3] and macdSignal[-1]>macdSignal[-3]\n",
    "\n",
    "short: macd[-1]<0 and macd[-2]>0 and macd[-1]<macd[-3] and macdSignal[-1]<macdSignal[-3]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "策略三\n",
    "long: macd[-1]<0 and secondGoldenCross\n",
    "short: macd[-1]>0 and secondDeathCross"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "策略四\n",
    "long: macd[-1]<0 and macdHistFiveUp\n",
    "\n",
    "short: macd[-1]>0 and macdHistFiveDn"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "策略五\n",
    "long: macd[-1]>0 and macdHistFiveDn and macdHistUp\n",
    "\n",
    "short: macd[-1]<0 and macdHistFiveUp and macdHistDn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、 进场&时间出场&&止损止盈\n",
    "    1. 将策略一在15Min周期下构建信号\n",
    "    2. 设置固定持有时间/设置止损止盈\n",
    "    3. 信号绩效评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\jaqs\\data\\py_expression_eval.py:477: RuntimeWarning: invalid value encountered in greater\n",
      "  res = arr > brr\n",
      "D:\\Anaconda3\\lib\\site-packages\\jaqs\\data\\py_expression_eval.py:504: RuntimeWarning: invalid value encountered in less_equal\n",
      "  res = arr <= brr\n",
      "D:\\Anaconda3\\lib\\site-packages\\jaqs\\data\\py_expression_eval.py:486: RuntimeWarning: invalid value encountered in less\n",
      "  res = arr < brr\n",
      "D:\\Anaconda3\\lib\\site-packages\\jaqs\\data\\py_expression_eval.py:495: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  res = arr >= brr\n"
     ]
    }
   ],
   "source": [
    "# 进场信号\n",
    "long = dv15.add_formula(\"long\",\"If(macd>0 && (Delay(macd,2)<=Delay(macdSignal,2) && macd>macdSignal),2,0)\", add_data=True)\n",
    "short = dv15.add_formula(\"short\",\"If(macd<0 && (Delay(macd,2)>=Delay(macdSignal,2) && macd<macdSignal),-2,0)\", add_data=True)\n",
    "\n",
    "# 出场信号\n",
    "close_long = dv15.add_formula(\"closeLong\",\"If(short==-2,1,0)\", add_data=True)\n",
    "close_short = dv15.add_formula(\"closeShort\",\"If(long==2,-1,0)\", add_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# long.iloc[800:-500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\importlib\\__init__.py:126: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  return _bootstrap._gcd_import(name[level:], package, level)\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:1405: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "  warnings.warn(_use_error_msg)\n"
     ]
    }
   ],
   "source": [
    "from jaqs_fxdayu.research import TimingDigger\n",
    "\n",
    "# step 1：实例化TimingDigger 通过output_folder和output_format指定测试报告的输出路径和输出格式，通过signal_name指定绩效文件名称\n",
    "td1 = TimingDigger(output_folder=\".\", output_format='pdf',signal_name='macdSignal1')\n",
    "tdSlTp = TimingDigger(output_folder=\".\", output_format='pdf',signal_name='macdSignal1SlTp')\n",
    "\n",
    "def TimingSignal(instantiation=td1, mhp=None, sl=None, tp=None):\n",
    "    #多空分别计算一遍 输出汇总结果\n",
    "    instantiation.process_signal(\n",
    "        enter_signal=dv15.get_ts(\"long\"),\n",
    "        exit_signal=dv15.get_ts(\"closeLong\"),\n",
    "        sig_type=\"long\",  # 信号类型 long/short\n",
    "        price=dv15.get_ts(\"close\"),\n",
    "        max_holding_period=mhp,  # 最大持有天数 可为空\n",
    "        stoploss=-sl if sl else None,  # 止损百分比 负数 可为空\n",
    "        stopprofit=None,  # 止盈百分比 正数 可为空\n",
    "    )\n",
    "\n",
    "    instantiation.process_signal(\n",
    "        enter_signal=dv15.get_ts(\"short\"),\n",
    "        exit_signal=dv15.get_ts(\"closeShort\"),\n",
    "        sig_type=\"short\",  # 信号类型 long/short\n",
    "        price=dv15.get_ts(\"close\"),\n",
    "        max_holding_period=mhp,  # 最大持有Bar数 可为空\n",
    "        stoploss=-sl if sl else None,  # 止损百分比 负数 可为空\n",
    "        stopprofit=None,  # 止盈百分比 正数 可为空\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# td1.process_signal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nan Data Count (should be zero) : 0;  Percentage of effective data: 4%\n",
      "Nan Data Count (should be zero) : 0;  Percentage of effective data: 4%\n"
     ]
    }
   ],
   "source": [
    "TimingSignal(td1, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved: C:\\Users\\small\\Desktop\\June2019\\时间序列课件\\HFDataviewSignalDigger\\macdSignal1/long_entry_exit_position/IF88_CTP.pdf\n",
      "*****-Summary-*****\n",
      "Event Analysis\n",
      "                       win    loss      all\n",
      "t-stat               8.458 -10.150    0.962\n",
      "p-value              0.000   0.000    0.338\n",
      "mean                 0.020  -0.012    0.002\n",
      "std                  0.017   0.010    0.021\n",
      "info_ratio           1.151  -1.222    0.086\n",
      "skewness             0.728  -1.183    0.706\n",
      "kurtosis            -0.879   0.540    0.323\n",
      "pct5                 0.002  -0.036   -0.032\n",
      "pct25                0.005  -0.017   -0.011\n",
      "pct50                0.014  -0.010   -0.002\n",
      "pct75                0.034  -0.005    0.012\n",
      "pct95                0.051  -0.002    0.048\n",
      "occurance           55.000  70.000  125.000\n",
      "win_ratio              NaN     NaN    0.440\n",
      "win_mean/loss_mean     NaN     NaN    1.608\n"
     ]
    }
   ],
   "source": [
    "td1.create_event_report(sig_type=\"long\",\n",
    "                       by_symbol=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved: C:\\Users\\small\\Desktop\\June2019\\时间序列课件\\HFDataviewSignalDigger\\macdSignal1/short_entry_exit_position/IF88_CTP.pdf\n",
      "*****-Summary-*****\n",
      "Event Analysis\n",
      "                       win    loss      all\n",
      "t-stat               8.879 -13.747    1.112\n",
      "p-value              0.000   0.000    0.268\n",
      "mean                 0.025  -0.009    0.002\n",
      "std                  0.017   0.006    0.019\n",
      "info_ratio           1.480  -1.598    0.106\n",
      "skewness             0.723  -0.344    1.348\n",
      "kurtosis            -0.298  -1.177    1.227\n",
      "pct5                 0.002  -0.019   -0.018\n",
      "pct25                0.017  -0.015   -0.010\n",
      "pct50                0.023  -0.008   -0.004\n",
      "pct75                0.026  -0.004    0.016\n",
      "pct95                0.057  -0.002    0.049\n",
      "occurance           37.000  75.000  112.000\n",
      "win_ratio              NaN     NaN    0.330\n",
      "win_mean/loss_mean     NaN     NaN    2.696\n"
     ]
    }
   ],
   "source": [
    "td1.create_event_report(sig_type=\"short\",\n",
    "                       by_symbol=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved: C:\\Users\\small\\Desktop\\June2019\\时间序列课件\\HFDataviewSignalDigger\\macdSignal1/long_short_entry_exit_position/IF88_CTP.pdf\n",
      "*****-Summary-*****\n",
      "Event Analysis\n",
      "                       win     loss      all\n",
      "t-stat              12.099  -15.445    1.457\n",
      "p-value              0.000    0.000    0.146\n",
      "mean                 0.022   -0.011    0.002\n",
      "std                  0.017    0.008    0.020\n",
      "info_ratio           1.268   -1.287    0.095\n",
      "skewness             0.693   -1.390    0.964\n",
      "kurtosis            -0.665    2.011    0.701\n",
      "pct5                 0.002   -0.029   -0.020\n",
      "pct25                0.006   -0.015   -0.011\n",
      "pct50                0.018   -0.009   -0.003\n",
      "pct75                0.033   -0.005    0.013\n",
      "pct95                0.054   -0.002    0.049\n",
      "occurance           92.000  145.000  237.000\n",
      "win_ratio              NaN      NaN    0.388\n",
      "win_mean/loss_mean     NaN      NaN    2.037\n"
     ]
    }
   ],
   "source": [
    "td1.create_event_report(sig_type=\"long_short\",\n",
    "                       by_symbol=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nan Data Count (should be zero) : 0;  Percentage of effective data: 4%\n",
      "Nan Data Count (should be zero) : 0;  Percentage of effective data: 4%\n"
     ]
    }
   ],
   "source": [
    "TimingSignal(tdSlTp, 50, 0.02, 0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure saved: C:\\Users\\small\\Desktop\\June2019\\时间序列课件\\HFDataviewSignalDigger\\macdSignal1SlTp/long_short_entry_exit_position/IF88_CTP.pdf\n",
      "*****-Summary-*****\n",
      "Event Analysis\n",
      "                       win     loss      all\n",
      "t-stat              12.383  -18.265    1.468\n",
      "p-value              0.000    0.000    0.144\n",
      "mean                 0.023   -0.011    0.002\n",
      "std                  0.017    0.007    0.020\n",
      "info_ratio           1.320   -1.506    0.096\n",
      "skewness             0.672   -0.452    1.131\n",
      "kurtosis            -0.697   -1.058    0.671\n",
      "pct5                 0.002   -0.022   -0.022\n",
      "pct25                0.008   -0.016   -0.011\n",
      "pct50                0.019   -0.009   -0.004\n",
      "pct75                0.033   -0.005    0.013\n",
      "pct95                0.054   -0.002    0.049\n",
      "occurance           89.000  148.000  237.000\n",
      "win_ratio              NaN      NaN    0.376\n",
      "win_mean/loss_mean     NaN      NaN    2.145\n"
     ]
    }
   ],
   "source": [
    "tdSlTp.create_event_report(sig_type=\"long_short\",\n",
    "                       by_symbol=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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